Improving the performance of AI models in tactical environments using a hybrid cloud architecture

Eric M. Sturzinger; Christopher J. Lowrance; Isaac J. Faber; Jason J. Choi; Alexander D. MacCalman

doi:10.1117/12.2587932

12 April 2021 Improving the performance of AI models in tactical environments using a hybrid cloud architecture

Eric M. Sturzinger, Christopher J. Lowrance, Isaac J. Faber, Jason J. Choi, Alexander D. MacCalman

Proceedings Volume 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III; 1174607 (2021) https://doi.org/10.1117/12.2587932
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

As the Department of Defense (DoD) looks to exploit and scale Artificial Intelligence (AI) capabilities across the warfighting domains, the Army plans to integrate advanced features into many of its combat systems. The benefits of cloud technologies offer promising solutions to these needs. While cloud-based AI-enabled capabilities leverage flexibility, common interfaces, and virtually infinite scale of resources, they suffer from their lack of proximity to the tactical edge. Tactical AI-enabled systems cannot reliably leverage advantages provided by cloud resources due to limited standardized practices for integration of on-premise/edge systems required by the deployed military. Future high-intensity conflict will be fought in a degraded, denied, intermittent, and lowbandwidth (DDIL) digital environment. As a result, tactical AI-enabled systems will be required to operate in a scenario where high speed, reliable cloud access is unavailable. This paper proposes a hybrid-cloud architecture that leverages resources of the cloud, when available, while also maintaining the capability to retrain tactical AI models in the field environment, using on-site computation and storage. The hybrid cloud construct consists of tactical cloud nodes that reside in closer proximity to AI-enabled systems at the edge. They may retain connectivity to the enterprise cloud yet have the ability to provide the common AI development platform and tool sets to support continuous integration, delivery, and deployment. Thus, its ultimate objective is to enable the seamless and expeditious operation of a distributed AI development environment for the Army and DoD that bridges the tactical edge and enterprise cloud.

Conference Presentation

Citation Download Citation

Eric M. Sturzinger, Christopher J. Lowrance, Isaac J. Faber, Jason J. Choi, and Alexander D. MacCalman "Improving the performance of AI models in tactical environments using a hybrid cloud architecture", Proc. SPIE 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III, 1174607 (12 April 2021); https://doi.org/10.1117/12.2587932

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